Challenges for Reinforcement Learning in Healthcare

no code implementations • 9 Mar 2021 • Elsa Riachi, Muhammad Mamdani, Michael Fralick, Frank Rudzicz

Many healthcare decisions involve navigating through a multitude of treatment options in a sequential and iterative manner to find an optimal treatment pathway with the goal of an optimal patient outcome.

reinforcement-learning Reinforcement Learning (RL)

A Real-World Demonstration of Machine Learning Generalizability: Intracranial Hemorrhage Detection on Head CT

no code implementations • 9 Feb 2021 • Hojjat Salehinejad, Jumpei Kitamura, Noah Ditkofsky, Amy Lin, Aditya Bharatha, Suradech Suthiphosuwan, Hui-Ming Lin, Jefferson R. Wilson, Muhammad Mamdani, Errol Colak

An ML model was trained using 21, 784 scans from the RSNA Intracranial Hemorrhage CT dataset while generalizability was evaluated using an external validation dataset obtained from our busy trauma and neurosurgical center.

BIG-bench Machine Learning Computed Tomography (CT) +1

Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging

no code implementations • 26 Oct 2020 • Hojjat Salehinejad, Edward Ho, Hui-Ming Lin, Priscila Crivellaro, Oleksandra Samorodova, Monica Tafur Arciniegas, Zamir Merali, Suradech Suthiphosuwan, Aditya Bharatha, Kristen Yeom, Muhammad Mamdani, Jefferson Wilson, Errol Colak

Fractures of the cervical spine are a medical emergency and may lead to permanent paralysis and even death.

Computed Tomography (CT) Management

Extracting relevant information from physician-patient dialogues for automated clinical note taking

no code implementations • WS 2019 • Serena Jeblee, Faiza Khan Khattak, Noah Crampton, Muhammad Mamdani, Frank Rudzicz

We present a system for automatically extracting pertinent medical information from dialogues between clinicians and patients.